| Citation: | HU H R，CHEN S X，WU H，et al. Continuous-discrete maximum correntropy CKF algorithm based on variational Bayes[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（10）：2859-2866 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0769
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To address the problems of unknown covariance of measurement noise and non-Gaussian mutation measurement noise in bearings-only target tracking, a square-root continuous-discrete variational Bayesian maximum correntropy cubature Kalman filter (SRCD-VBMCCKF) algorithm is proposed. Firstly, the target tracking model is established as a continuous state space-discrete measurement space model, which improves the accuracy of target tracking; secondly, the unknown time-varying measurement noise is estimated by the variational Bayes criterion, which improves the adaptability of the algorithm; finally, considering the non-Gaussian mutation noise in the measurement, the robustness factor is constructed by the maximum correntropy criterion, which further enhances the algorithm’s robustness to abnormal measurements. The simulation results show that the proposed algorithm can effectively suppress the unknown time-varying noise and non-Gaussian heavy-tail mutation noise in the measurement. Compared with the traditional filtering algorithm, the proposed algorithm is both adaptive and robust.
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